info.print_info() # set action and state action_size, state_size = info.getInfo() # prepare for training env_info = env.reset(train_mode=False)[brain_name] # reset the environment # Create the agent agent = Agent(state_size=state_size, action_size=action_size, seed=0) # train the agent scores = agent.dqn(env=env, brain_name=brain_name, n_episodes=2000, max_t=1000, eps_start=1.0, eps_end=0.01, eps_decay=0.995) # plot the scores fig = plt.figure() ax = fig.add_subplot(111) plt.plot(np.arange(len(scores)), scores) plt.ylabel('Score') plt.xlabel('Episode #') plt.show() # test best agent score = agent.test(env, brain_name) print("Score: {}".format(score))
from unityagents import UnityEnvironment import numpy as np import torch import matplotlib.pyplot as plt from info import Info from dqn_agent import Agent # Change File Name to the path/to/Banana.exe env = UnityEnvironment( file_name= "C:/Udacity/Deep Reinforcement Learning/deep-reinforcement-learning/p1_navigation/Banana_Windows_x86_64/Banana.exe" ) # get the default brain brain_name = env.brain_names[0] brain = env.brains[brain_name] info = Info(env, brain_name, brain) # print out information info.print_info() # set action and state action_size, state_size = info.getInfo() # prepare for training env_info = env.reset(train_mode=False)[brain_name] # reset the environment # Create the agent agent = Agent(state_size=state_size, action_size=action_size, seed=0) # test best agent agent.test(env, brain_name)